세계의 농업 분야 인공지능 시장 : 부문별 예측(2025-2030년)

The Artificial Intelligence in Agriculture Market was valued at USD 2.25 billion in 2023, expected to reach USD 2.73 billion in 2024, and is projected to grow at a CAGR of 22.45%, to USD 9.29 billion by 2030.

Artificial intelligence (AI) in agriculture encompasses the use of advanced computational technologies to enhance farming processes, improve productivity, and solve environmental issues. The scope of AI in this field is broad, spanning from automated monitoring systems and predictive analytics for crop management to robotics for precision farming and supply chain optimization. The necessity of AI arises from the urgent need to enhance food security, manage resource limitations, and mitigate the impacts of climate change on agriculture. Applications are diverse, including automated weed and pest detection, yield prediction, and soil monitoring. End-use sectors range from large-scale agribusinesses to small-holder farmers, leveraging AI for improved decision-making and operational efficiency. Key growth factors include the increasing global demand for food, the need for sustainable agriculture practices, and technological advancements in AI and IoT. Opportunities abound in developing AI solutions tailored for climate-resilient crops and precision irrigation, potentially unlocking new revenue streams. Collaborations between tech companies and agricultural businesses can foster innovation, creating AI tools that are accessible to farmers of all scales. However, challenges such as high implementation costs, limited technology adoption in developing regions, and data privacy concerns could hinder market growth. Infrastructure inadequacies and skill gaps further complicate the diffusion of AI solutions in rural areas. To capitalize on growth, research and innovation should focus on low-cost AI systems and adaptable technologies for varied agricultural conditions. AI's integration with blockchain for supply chain transparency and with biotechnology for genetic crop improvement presents lucrative areas for exploration. The nature of the market is dynamic, with rapid technological changes necessitating agility and foresight from stakeholders. Addressing market challenges involves concerted efforts in policy-making, education on AI benefits, and investment in supportive infrastructure, paving the way for a transformative agricultural landscape empowered by AI.

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Artificial Intelligence in Agriculture Market

The Artificial Intelligence in Agriculture Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

• Market Drivers
  • Growing trend of predictive analytics to enhance agricultural productivity
  • Favorable government and private organizations initiatives to adopt modern agricultural technologies
  • Escalating adoption of advanced unmanned aerial vehicles (UAVs), and satellite imaging systems across agriculture sector
• Market Restraints
  • Constraints associated with the high cost of deployment of artificial intelligence (AI) in precision farming equipment
• Market Opportunities
  • Emerging application of AI based agriculture in livestock monitoring and precision farming
  • Integration of advanced farming technologies including big data, and IoT sensors with artificial intelligence
• Market Challenges
  • Lack of awareness and limited availability of skilled professionals

Porter's Five Forces: A Strategic Tool for Navigating the Artificial Intelligence in Agriculture Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Artificial Intelligence in Agriculture Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Artificial Intelligence in Agriculture Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Artificial Intelligence in Agriculture Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Artificial Intelligence in Agriculture Market

A detailed market share analysis in the Artificial Intelligence in Agriculture Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Artificial Intelligence in Agriculture Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Artificial Intelligence in Agriculture Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Artificial Intelligence in Agriculture Market

A strategic analysis of the Artificial Intelligence in Agriculture Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Artificial Intelligence in Agriculture Market, highlighting leading vendors and their innovative profiles. These include Ag Code by Wilbur-Ellis Holdings, Inc., AGCO Corporation, AgEagle Aerial Systems Inc., AgNext, Apro Software, Bayer AG., Cainthus by Ever.Ag, ClimateAi, inc., Corteva Agriscience by Albaugh, LLC, Cropin Technology Solutions Pvt Ltd., CropX Technologies Ltd., DeHaat by Green Agrevolution PVT. LTD, Descartes Labs, Inc. by Antarctica Capital, FarmBot, Inc., Farmers Edge Inc., Gamaya Inc., Gro Intelligence, Inc., Infosys Limited, Intellias, LLC, Intello Labs Private Limited, International Business Machines Corporation, John Deere Group, Keenethics., Khetibuddy Agritech Private Limited., Microsoft Corporation, PrecisionHawk Inc., Raven Industries, Inc., Trace Genomics, Inc., Trimble Inc., Tule Technologies Inc., and Wipro Limited.

Market Segmentation & Coverage

This research report categorizes the Artificial Intelligence in Agriculture Market to forecast the revenues and analyze trends in each of the following sub-markets:

• Based on Offering, market is studied across Hardware, Services, and Software.
• Based on Technology, market is studied across Computer Vision, Machine Learning, and Predictive Analytics.
• Based on Deployment, market is studied across Cloud, Hybrid, and On-premise.
• Based on Application, market is studied across Agriculture Robots, Drone Analytics, Labor Management, Livestock Monitoring, and Precision Farming.
• Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

• 2.1. Define: Research Objective
• 2.2. Determine: Research Design
• 2.3. Prepare: Research Instrument
• 2.4. Collect: Data Source
• 2.5. Analyze: Data Interpretation
• 2.6. Formulate: Data Verification
• 2.7. Publish: Research Report
• 2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Market Dynamics
  • 5.1.1. Drivers
    • 5.1.1.1. Growing trend of predictive analytics to enhance agricultural productivity
    • 5.1.1.2. Favorable government and private organizations initiatives to adopt modern

agricultural technologies

• 5.1.1.3. Escalating adoption of advanced unmanned aerial vehicles (UAVs), and satellite imaging systems across agriculture sector
• 5.1.2. Restraints
  • 5.1.2.1. Constraints associated with the high cost of deployment of artificial intelligence (AI) in precision farming equipment
• 5.1.3. Opportunities
  • 5.1.3.1. Emerging application of AI based agriculture in livestock monitoring and precision farming
  • 5.1.3.2. Integration of advanced farming technologies including big data, and IoT sensors with artificial intelligence
• 5.1.4. Challenges
  • 5.1.4.1. Lack of awareness and limited availability of skilled professionals
• 5.2. Market Segmentation Analysis
• 5.3. Porter's Five Forces Analysis
  • 5.3.1. Threat of New Entrants
  • 5.3.2. Threat of Substitutes
  • 5.3.3. Bargaining Power of Customers
  • 5.3.4. Bargaining Power of Suppliers
  • 5.3.5. Industry Rivalry
• 5.4. PESTLE Analysis
  • 5.4.1. Political
  • 5.4.2. Economic
  • 5.4.3. Social
  • 5.4.4. Technological
  • 5.4.5. Legal
  • 5.4.6. Environmental

6. Artificial Intelligence in Agriculture Market, by Offering

• 6.1. Introduction
• 6.2. Hardware
• 6.3. Services
• 6.4. Software

7. Artificial Intelligence in Agriculture Market, by Technology

• 7.1. Introduction
• 7.2. Computer Vision
• 7.3. Machine Learning
• 7.4. Predictive Analytics

8. Artificial Intelligence in Agriculture Market, by Deployment

• 8.1. Introduction
• 8.2. Cloud
• 8.3. Hybrid
• 8.4. On-premise

9. Artificial Intelligence in Agriculture Market, by Application

• 9.1. Introduction
• 9.2. Agriculture Robots
• 9.3. Drone Analytics
• 9.4. Labor Management
• 9.5. Livestock Monitoring
• 9.6. Precision Farming

10. Americas Artificial Intelligence in Agriculture Market

• 10.1. Introduction
• 10.2. Argentina
• 10.3. Brazil
• 10.4. Canada
• 10.5. Mexico
• 10.6. United States

11. Asia-Pacific Artificial Intelligence in Agriculture Market

• 11.1. Introduction
• 11.2. Australia
• 11.3. China
• 11.4. India
• 11.5. Indonesia
• 11.6. Japan
• 11.7. Malaysia
• 11.8. Philippines
• 11.9. Singapore
• 11.10. South Korea
• 11.11. Taiwan
• 11.12. Thailand
• 11.13. Vietnam

12. Europe, Middle East & Africa Artificial Intelligence in Agriculture Market

• 12.1. Introduction
• 12.2. Denmark
• 12.3. Egypt
• 12.4. Finland
• 12.5. France
• 12.6. Germany
• 12.7. Israel
• 12.8. Italy
• 12.9. Netherlands
• 12.10. Nigeria
• 12.11. Norway
• 12.12. Poland
• 12.13. Qatar
• 12.14. Russia
• 12.15. Saudi Arabia
• 12.16. South Africa
• 12.17. Spain
• 12.18. Sweden
• 12.19. Switzerland
• 12.20. Turkey
• 12.21. United Arab Emirates
• 12.22. United Kingdom

13. Competitive Landscape

• 13.1. Market Share Analysis, 2023
• 13.2. FPNV Positioning Matrix, 2023
• 13.3. Competitive Scenario Analysis
  • 13.3.1. SLCM's AI-Based App Agri Reach Gets NABL Accreditation
  • 13.3.2. Wadhwani AI Signs MoU with Karnataka Government to Promote Welfare for Farmers
  • 13.3.3. Astanor Ventures Leads USD 23 Million Series A for Source.Ag's Greenhouse System
  • 13.3.4. U.S. EU AI Agreement: U.S. and EU to Launch First-of-its-Kind Artificial Intelligence Agreement
  • 13.3.5. Wadhwani AI Gets USD 1 Million Grant from Google.org to Build AI Solutions in Agriculture
  • 13.3.6. UAE Launches New AI-Powered Mobile App for Crop Disorder Detection
  • 13.3.7. Syngenta, Plantix Launch AI Farming Tools for Farmers Across Asia
  • 13.3.8. Agreena Acquires Hummingbird Technologies to Strengthen Carbon Farming
  • 13.3.9. Cropin Plans to Launch World's First Agri Intelligence Cloud- 'Agcloud' Soon
  • 13.3.10. AiDash Buys Farming AI Platform Neurafarms.ai
• 13.4. Strategy Analysis & Recommendation

Companies Mentioned

• 1. Ag Code by Wilbur-Ellis Holdings, Inc.
• 2. AGCO Corporation
• 3. AgEagle Aerial Systems Inc.
• 4. AgNext
• 5. Apro Software
• 6. Bayer AG.
• 7. Cainthus by Ever.Ag
• 8. ClimateAi, inc.
• 9. Corteva Agriscience by Albaugh, LLC
• 10. Cropin Technology Solutions Pvt Ltd.
• 11. CropX Technologies Ltd.
• 12. DeHaat by Green Agrevolution PVT. LTD
• 13. Descartes Labs, Inc. by Antarctica Capital
• 14. FarmBot, Inc.
• 15. Farmers Edge Inc.
• 16. Gamaya Inc.
• 17. Gro Intelligence, Inc.
• 18. Infosys Limited
• 19. Intellias, LLC
• 20. Intello Labs Private Limited
• 21. International Business Machines Corporation
• 22. John Deere Group
• 23. Keenethics.
• 24. Khetibuddy Agritech Private Limited.
• 25. Microsoft Corporation
• 26. PrecisionHawk Inc.
• 27. Raven Industries, Inc.
• 28. Trace Genomics, Inc.
• 29. Trimble Inc.
• 30. Tule Technologies Inc.
• 31. Wipro Limited